Overview

Dataset statistics

Number of variables31
Number of observations598968
Missing cells3143261
Missing cells (%)16.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory141.7 MiB
Average record size in memory248.0 B

Variable types

Numeric20
DateTime1
Categorical4
Text6

Alerts

CANCELLED is highly imbalanced (96.6%)Imbalance
DIVERTED is highly imbalanced (98.5%)Imbalance
CANCELLATION_CODE has 596828 (99.6%) missing valuesMissing
CARRIER_DELAY has 505983 (84.5%) missing valuesMissing
WEATHER_DELAY has 505983 (84.5%) missing valuesMissing
NAS_DELAY has 505983 (84.5%) missing valuesMissing
SECURITY_DELAY has 505983 (84.5%) missing valuesMissing
LATE_AIRCRAFT_DELAY has 505983 (84.5%) missing valuesMissing
WEATHER_DELAY is highly skewed (γ1 = 24.19997941)Skewed
SECURITY_DELAY is highly skewed (γ1 = 37.11256308)Skewed
DEP_DELAY has 28192 (4.7%) zerosZeros
ARR_DELAY has 11398 (1.9%) zerosZeros
CARRIER_DELAY has 38137 (6.4%) zerosZeros
WEATHER_DELAY has 90098 (15.0%) zerosZeros
NAS_DELAY has 52619 (8.8%) zerosZeros
SECURITY_DELAY has 92440 (15.4%) zerosZeros
LATE_AIRCRAFT_DELAY has 45634 (7.6%) zerosZeros

Reproduction

Analysis started2024-03-30 06:00:35.173897
Analysis finished2024-03-30 06:03:28.606668
Duration2 minutes and 53.43 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

DAY_OF_WEEK
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9208472
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:28.712325image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.0780545
Coefficient of variation (CV)0.53000139
Kurtosis-1.3112484
Mean3.9208472
Median Absolute Deviation (MAD)2
Skewness0.072530399
Sum2348462
Variance4.3183103
MonotonicityIncreasing
2024-03-30T03:03:28.978107image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 100100
16.7%
7 98748
16.5%
2 92434
15.4%
4 81685
13.6%
5 81303
13.6%
3 76395
12.8%
6 68303
11.4%
ValueCountFrequency (%)
1 100100
16.7%
2 92434
15.4%
3 76395
12.8%
4 81685
13.6%
5 81303
13.6%
6 68303
11.4%
7 98748
16.5%
ValueCountFrequency (%)
7 98748
16.5%
6 68303
11.4%
5 81303
13.6%
4 81685
13.6%
3 76395
12.8%
2 92434
15.4%
1 100100
16.7%
Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
Minimum2023-10-01 00:00:00
Maximum2023-10-31 00:00:00
2024-03-30T03:03:29.255296image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:29.575072image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
WN
128588 
DL
85367 
AA
81344 
UA
65896 
OO
59863 
Other values (10)
177910 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters1197936
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9E
2nd row9E
3rd row9E
4th row9E
5th row9E

Common Values

ValueCountFrequency (%)
WN 128588
21.5%
DL 85367
14.3%
AA 81344
13.6%
UA 65896
11.0%
OO 59863
10.0%
YX 23589
 
3.9%
NK 23294
 
3.9%
B6 22066
 
3.7%
AS 20306
 
3.4%
MQ 20254
 
3.4%
Other values (5) 68401
11.4%

Length

2024-03-30T03:03:29.804858image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wn 128588
21.5%
dl 85367
14.3%
aa 81344
13.6%
ua 65896
11.0%
oo 59863
10.0%
yx 23589
 
3.9%
nk 23294
 
3.9%
b6 22066
 
3.7%
as 20306
 
3.4%
mq 20254
 
3.4%
Other values (5) 68401
11.4%

Most occurring characters

ValueCountFrequency (%)
A 255805
21.4%
N 151882
12.7%
O 136503
11.4%
W 128588
10.7%
D 85367
 
7.1%
L 85367
 
7.1%
U 65896
 
5.5%
9 35066
 
2.9%
H 23692
 
2.0%
Y 23589
 
2.0%
Other values (11) 206181
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1197936
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 255805
21.4%
N 151882
12.7%
O 136503
11.4%
W 128588
10.7%
D 85367
 
7.1%
L 85367
 
7.1%
U 65896
 
5.5%
9 35066
 
2.9%
H 23692
 
2.0%
Y 23589
 
2.0%
Other values (11) 206181
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1197936
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 255805
21.4%
N 151882
12.7%
O 136503
11.4%
W 128588
10.7%
D 85367
 
7.1%
L 85367
 
7.1%
U 65896
 
5.5%
9 35066
 
2.9%
H 23692
 
2.0%
Y 23589
 
2.0%
Other values (11) 206181
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1197936
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 255805
21.4%
N 151882
12.7%
O 136503
11.4%
W 128588
10.7%
D 85367
 
7.1%
L 85367
 
7.1%
U 65896
 
5.5%
9 35066
 
2.9%
H 23692
 
2.0%
Y 23589
 
2.0%
Other values (11) 206181
17.2%

OP_CARRIER_FL_NUM
Real number (ℝ)

Distinct5870
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2328.4011
Minimum1
Maximum8819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:30.167246image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile298
Q11074
median2090
Q33390
95-th percentile5362
Maximum8819
Range8818
Interquartile range (IQR)2316

Descriptive statistics

Standard deviation1556.4399
Coefficient of variation (CV)0.66845866
Kurtosis-0.61349446
Mean2328.4011
Median Absolute Deviation (MAD)1110
Skewness0.57431447
Sum1.3946378 × 109
Variance2422505.2
MonotonicityNot monotonic
2024-03-30T03:03:30.492156image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1168 322
 
0.1%
1015 307
 
0.1%
346 299
 
< 0.1%
648 298
 
< 0.1%
2087 294
 
< 0.1%
533 293
 
< 0.1%
371 287
 
< 0.1%
498 286
 
< 0.1%
438 286
 
< 0.1%
368 284
 
< 0.1%
Other values (5860) 596012
99.5%
ValueCountFrequency (%)
1 212
< 0.1%
2 180
< 0.1%
3 122
< 0.1%
4 110
< 0.1%
5 97
< 0.1%
6 77
 
< 0.1%
7 173
< 0.1%
8 152
< 0.1%
9 164
< 0.1%
10 212
< 0.1%
ValueCountFrequency (%)
8819 1
 
< 0.1%
8811 3
< 0.1%
8810 1
 
< 0.1%
8801 1
 
< 0.1%
8800 1
 
< 0.1%
8789 1
 
< 0.1%
8786 1
 
< 0.1%
8785 3
< 0.1%
8784 4
< 0.1%
8783 6
< 0.1%

ORIGIN_AIRPORT_ID
Real number (ℝ)

Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12637.91
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:30.775630image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1530.5187
Coefficient of variation (CV)0.12110536
Kurtosis-1.3005734
Mean12637.91
Median Absolute Deviation (MAD)1591
Skewness0.115836
Sum7.5697036 × 109
Variance2342487.4
MonotonicityNot monotonic
2024-03-30T03:03:31.089131image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 29251
 
4.9%
11298 25786
 
4.3%
11292 25543
 
4.3%
13930 22812
 
3.8%
12889 17503
 
2.9%
11057 17372
 
2.9%
12892 16823
 
2.8%
14107 15503
 
2.6%
14747 14313
 
2.4%
13204 13927
 
2.3%
Other values (326) 400135
66.8%
ValueCountFrequency (%)
10135 408
 
0.1%
10136 142
 
< 0.1%
10140 2370
0.4%
10141 62
 
< 0.1%
10146 62
 
< 0.1%
10154 100
 
< 0.1%
10155 90
 
< 0.1%
10157 155
 
< 0.1%
10158 275
 
< 0.1%
10165 8
 
< 0.1%
ValueCountFrequency (%)
16869 155
 
< 0.1%
16218 138
 
< 0.1%
15991 62
 
< 0.1%
15919 1029
0.2%
15897 21
 
< 0.1%
15841 62
 
< 0.1%
15624 800
0.1%
15607 62
 
< 0.1%
15582 53
 
< 0.1%
15569 54
 
< 0.1%

ORIGIN
Text

Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:31.723664image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1796904
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCVG
2nd rowCVG
3rd rowCVG
4th rowCVG
5th rowROC
ValueCountFrequency (%)
atl 29251
 
4.9%
dfw 25786
 
4.3%
den 25543
 
4.3%
ord 22812
 
3.8%
las 17503
 
2.9%
clt 17372
 
2.9%
lax 16823
 
2.8%
phx 15503
 
2.6%
sea 14313
 
2.4%
mco 13927
 
2.3%
Other values (326) 400135
66.8%
2024-03-30T03:03:32.695334image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 204645
 
11.4%
L 166290
 
9.3%
S 153627
 
8.5%
D 143200
 
8.0%
T 95426
 
5.3%
O 91602
 
5.1%
C 89822
 
5.0%
M 80121
 
4.5%
F 74343
 
4.1%
W 71138
 
4.0%
Other values (16) 626690
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 204645
 
11.4%
L 166290
 
9.3%
S 153627
 
8.5%
D 143200
 
8.0%
T 95426
 
5.3%
O 91602
 
5.1%
C 89822
 
5.0%
M 80121
 
4.5%
F 74343
 
4.1%
W 71138
 
4.0%
Other values (16) 626690
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 204645
 
11.4%
L 166290
 
9.3%
S 153627
 
8.5%
D 143200
 
8.0%
T 95426
 
5.3%
O 91602
 
5.1%
C 89822
 
5.0%
M 80121
 
4.5%
F 74343
 
4.1%
W 71138
 
4.0%
Other values (16) 626690
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 204645
 
11.4%
L 166290
 
9.3%
S 153627
 
8.5%
D 143200
 
8.0%
T 95426
 
5.3%
O 91602
 
5.1%
C 89822
 
5.0%
M 80121
 
4.5%
F 74343
 
4.1%
W 71138
 
4.0%
Other values (16) 626690
34.9%
Distinct330
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:33.234045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.056866
Min length8

Characters and Unicode

Total characters7820645
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCincinnati, OH
2nd rowCincinnati, OH
3rd rowCincinnati, OH
4th rowCincinnati, OH
5th rowRochester, NY
ValueCountFrequency (%)
tx 64857
 
4.7%
ca 64080
 
4.6%
fl 49856
 
3.6%
ny 31859
 
2.3%
il 31509
 
2.3%
ga 31321
 
2.2%
san 30692
 
2.2%
chicago 30465
 
2.2%
atlanta 29251
 
2.1%
new 29147
 
2.1%
Other values (401) 1000459
71.8%
2024-03-30T03:03:34.099940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
794528
 
10.2%
, 598968
 
7.7%
a 596417
 
7.6%
o 432105
 
5.5%
e 411661
 
5.3%
n 382827
 
4.9%
t 374515
 
4.8%
l 347670
 
4.4%
i 299544
 
3.8%
r 281513
 
3.6%
Other values (47) 3300897
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7820645
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
794528
 
10.2%
, 598968
 
7.7%
a 596417
 
7.6%
o 432105
 
5.5%
e 411661
 
5.3%
n 382827
 
4.9%
t 374515
 
4.8%
l 347670
 
4.4%
i 299544
 
3.8%
r 281513
 
3.6%
Other values (47) 3300897
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7820645
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
794528
 
10.2%
, 598968
 
7.7%
a 596417
 
7.6%
o 432105
 
5.5%
e 411661
 
5.3%
n 382827
 
4.9%
t 374515
 
4.8%
l 347670
 
4.4%
i 299544
 
3.8%
r 281513
 
3.6%
Other values (47) 3300897
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7820645
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
794528
 
10.2%
, 598968
 
7.7%
a 596417
 
7.6%
o 432105
 
5.5%
e 411661
 
5.3%
n 382827
 
4.9%
t 374515
 
4.8%
l 347670
 
4.4%
i 299544
 
3.8%
r 281513
 
3.6%
Other values (47) 3300897
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:34.743064image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1545575
Min length4

Characters and Unicode

Total characters4884319
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKentucky
2nd rowKentucky
3rd rowKentucky
4th rowKentucky
5th rowNew York
ValueCountFrequency (%)
texas 64857
 
9.5%
california 64080
 
9.3%
florida 49856
 
7.3%
new 47383
 
6.9%
york 31859
 
4.6%
illinois 31509
 
4.6%
carolina 31341
 
4.6%
georgia 31321
 
4.6%
colorado 27833
 
4.1%
north 27527
 
4.0%
Other values (51) 277837
40.5%
2024-03-30T03:03:35.465338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 657006
13.5%
i 546765
 
11.2%
o 464458
 
9.5%
n 365231
 
7.5%
r 350696
 
7.2%
e 304645
 
6.2%
s 282628
 
5.8%
l 268920
 
5.5%
C 125080
 
2.6%
d 116330
 
2.4%
Other values (37) 1402560
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4884319
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 657006
13.5%
i 546765
 
11.2%
o 464458
 
9.5%
n 365231
 
7.5%
r 350696
 
7.2%
e 304645
 
6.2%
s 282628
 
5.8%
l 268920
 
5.5%
C 125080
 
2.6%
d 116330
 
2.4%
Other values (37) 1402560
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4884319
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 657006
13.5%
i 546765
 
11.2%
o 464458
 
9.5%
n 365231
 
7.5%
r 350696
 
7.2%
e 304645
 
6.2%
s 282628
 
5.8%
l 268920
 
5.5%
C 125080
 
2.6%
d 116330
 
2.4%
Other values (37) 1402560
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4884319
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 657006
13.5%
i 546765
 
11.2%
o 464458
 
9.5%
n 365231
 
7.5%
r 350696
 
7.2%
e 304645
 
6.2%
s 282628
 
5.8%
l 268920
 
5.5%
C 125080
 
2.6%
d 116330
 
2.4%
Other values (37) 1402560
28.7%

ORIGIN_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.88361
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:35.796563image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median51
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.587782
Coefficient of variation (CV)0.48443938
Kurtosis-1.3201732
Mean54.88361
Median Absolute Deviation (MAD)23
Skewness-0.036561168
Sum32873526
Variance706.91015
MonotonicityNot monotonic
2024-03-30T03:03:36.165262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 64857
 
10.8%
91 64080
 
10.7%
33 49856
 
8.3%
22 31859
 
5.3%
41 31509
 
5.3%
34 31321
 
5.2%
82 27833
 
4.6%
36 26054
 
4.3%
38 20741
 
3.5%
85 19259
 
3.2%
Other values (42) 231599
38.7%
ValueCountFrequency (%)
1 2684
 
0.4%
2 10886
1.8%
3 2584
 
0.4%
4 289
 
< 0.1%
5 102
 
< 0.1%
11 1826
 
0.3%
12 1534
 
0.3%
13 12616
2.1%
14 617
 
0.1%
15 1196
 
0.2%
ValueCountFrequency (%)
93 16666
 
2.8%
92 6792
 
1.1%
91 64080
10.7%
88 635
 
0.1%
87 10088
 
1.7%
86 2623
 
0.4%
85 19259
 
3.2%
84 1861
 
0.3%
83 2522
 
0.4%
82 27833
4.6%

DEST_AIRPORT_ID
Real number (ℝ)

Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12637.898
Minimum10135
Maximum16869
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:36.465495image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum10135
5-th percentile10397
Q111292
median12889
Q314027
95-th percentile14893
Maximum16869
Range6734
Interquartile range (IQR)2735

Descriptive statistics

Standard deviation1530.5265
Coefficient of variation (CV)0.1211061
Kurtosis-1.3005126
Mean12637.898
Median Absolute Deviation (MAD)1591
Skewness0.11589113
Sum7.5696965 × 109
Variance2342511.4
MonotonicityNot monotonic
2024-03-30T03:03:36.811444image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10397 29252
 
4.9%
11298 25786
 
4.3%
11292 25544
 
4.3%
13930 22798
 
3.8%
12889 17498
 
2.9%
11057 17381
 
2.9%
12892 16835
 
2.8%
14107 15507
 
2.6%
14747 14309
 
2.4%
13204 13936
 
2.3%
Other values (326) 400122
66.8%
ValueCountFrequency (%)
10135 407
 
0.1%
10136 143
 
< 0.1%
10140 2369
0.4%
10141 62
 
< 0.1%
10146 62
 
< 0.1%
10154 100
 
< 0.1%
10155 90
 
< 0.1%
10157 155
 
< 0.1%
10158 275
 
< 0.1%
10165 8
 
< 0.1%
ValueCountFrequency (%)
16869 155
 
< 0.1%
16218 139
 
< 0.1%
15991 62
 
< 0.1%
15919 1030
0.2%
15897 21
 
< 0.1%
15841 62
 
< 0.1%
15624 800
0.1%
15607 62
 
< 0.1%
15582 53
 
< 0.1%
15569 54
 
< 0.1%

DEST
Text

Distinct336
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:37.494690image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1796904
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJFK
2nd rowMSP
3rd rowDTW
4th rowDTW
5th rowDTW
ValueCountFrequency (%)
atl 29252
 
4.9%
dfw 25786
 
4.3%
den 25544
 
4.3%
ord 22798
 
3.8%
las 17498
 
2.9%
clt 17381
 
2.9%
lax 16835
 
2.8%
phx 15507
 
2.6%
sea 14309
 
2.4%
mco 13936
 
2.3%
Other values (326) 400122
66.8%
2024-03-30T03:03:38.488108image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 204662
 
11.4%
L 166294
 
9.3%
S 153633
 
8.5%
D 143177
 
8.0%
T 95434
 
5.3%
O 91586
 
5.1%
C 89845
 
5.0%
M 80124
 
4.5%
F 74336
 
4.1%
W 71131
 
4.0%
Other values (16) 626682
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 204662
 
11.4%
L 166294
 
9.3%
S 153633
 
8.5%
D 143177
 
8.0%
T 95434
 
5.3%
O 91586
 
5.1%
C 89845
 
5.0%
M 80124
 
4.5%
F 74336
 
4.1%
W 71131
 
4.0%
Other values (16) 626682
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 204662
 
11.4%
L 166294
 
9.3%
S 153633
 
8.5%
D 143177
 
8.0%
T 95434
 
5.3%
O 91586
 
5.1%
C 89845
 
5.0%
M 80124
 
4.5%
F 74336
 
4.1%
W 71131
 
4.0%
Other values (16) 626682
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 204662
 
11.4%
L 166294
 
9.3%
S 153633
 
8.5%
D 143177
 
8.0%
T 95434
 
5.3%
O 91586
 
5.1%
C 89845
 
5.0%
M 80124
 
4.5%
F 74336
 
4.1%
W 71131
 
4.0%
Other values (16) 626682
34.9%
Distinct330
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:39.093857image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length34
Median length29
Mean length13.05693
Min length8

Characters and Unicode

Total characters7820683
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York, NY
2nd rowMinneapolis, MN
3rd rowDetroit, MI
4th rowDetroit, MI
5th rowDetroit, MI
ValueCountFrequency (%)
tx 64878
 
4.7%
ca 64099
 
4.6%
fl 49872
 
3.6%
ny 31856
 
2.3%
il 31496
 
2.3%
ga 31324
 
2.2%
san 30691
 
2.2%
chicago 30452
 
2.2%
atlanta 29252
 
2.1%
new 29143
 
2.1%
Other values (401) 1000454
71.8%
2024-03-30T03:03:40.002667image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
794549
 
10.2%
, 598968
 
7.7%
a 596409
 
7.6%
o 432107
 
5.5%
e 411679
 
5.3%
n 382866
 
4.9%
t 374545
 
4.8%
l 347680
 
4.4%
i 299517
 
3.8%
r 281506
 
3.6%
Other values (47) 3300857
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7820683
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
794549
 
10.2%
, 598968
 
7.7%
a 596409
 
7.6%
o 432107
 
5.5%
e 411679
 
5.3%
n 382866
 
4.9%
t 374545
 
4.8%
l 347680
 
4.4%
i 299517
 
3.8%
r 281506
 
3.6%
Other values (47) 3300857
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7820683
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
794549
 
10.2%
, 598968
 
7.7%
a 596409
 
7.6%
o 432107
 
5.5%
e 411679
 
5.3%
n 382866
 
4.9%
t 374545
 
4.8%
l 347680
 
4.4%
i 299517
 
3.8%
r 281506
 
3.6%
Other values (47) 3300857
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7820683
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
794549
 
10.2%
, 598968
 
7.7%
a 596409
 
7.6%
o 432107
 
5.5%
e 411679
 
5.3%
n 382866
 
4.9%
t 374545
 
4.8%
l 347680
 
4.4%
i 299517
 
3.8%
r 281506
 
3.6%
Other values (47) 3300857
42.2%
Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:40.422473image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length46
Median length14
Mean length8.1544072
Min length4

Characters and Unicode

Total characters4884229
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York
2nd rowMinnesota
3rd rowMichigan
4th rowMichigan
5th rowMichigan
ValueCountFrequency (%)
texas 64878
 
9.5%
california 64099
 
9.4%
florida 49872
 
7.3%
new 47375
 
6.9%
york 31856
 
4.6%
illinois 31496
 
4.6%
carolina 31344
 
4.6%
georgia 31324
 
4.6%
colorado 27834
 
4.1%
north 27530
 
4.0%
Other values (51) 277790
40.5%
2024-03-30T03:03:41.068215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 657009
13.5%
i 546759
 
11.2%
o 464481
 
9.5%
n 365202
 
7.5%
r 350731
 
7.2%
e 304645
 
6.2%
s 282584
 
5.8%
l 268912
 
5.5%
C 125100
 
2.6%
d 116336
 
2.4%
Other values (37) 1402470
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4884229
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 657009
13.5%
i 546759
 
11.2%
o 464481
 
9.5%
n 365202
 
7.5%
r 350731
 
7.2%
e 304645
 
6.2%
s 282584
 
5.8%
l 268912
 
5.5%
C 125100
 
2.6%
d 116336
 
2.4%
Other values (37) 1402470
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4884229
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 657009
13.5%
i 546759
 
11.2%
o 464481
 
9.5%
n 365202
 
7.5%
r 350731
 
7.2%
e 304645
 
6.2%
s 282584
 
5.8%
l 268912
 
5.5%
C 125100
 
2.6%
d 116336
 
2.4%
Other values (37) 1402470
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4884229
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 657009
13.5%
i 546759
 
11.2%
o 464481
 
9.5%
n 365202
 
7.5%
r 350731
 
7.2%
e 304645
 
6.2%
s 282584
 
5.8%
l 268912
 
5.5%
C 125100
 
2.6%
d 116336
 
2.4%
Other values (37) 1402470
28.7%

DEST_WAC
Real number (ℝ)

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.886885
Minimum1
Maximum93
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:41.402262image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q134
median51
Q382
95-th percentile91
Maximum93
Range92
Interquartile range (IQR)48

Descriptive statistics

Standard deviation26.587339
Coefficient of variation (CV)0.48440239
Kurtosis-1.3202052
Mean54.886885
Median Absolute Deviation (MAD)23
Skewness-0.03670187
Sum32875488
Variance706.88657
MonotonicityNot monotonic
2024-03-30T03:03:41.694791image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 64878
 
10.8%
91 64099
 
10.7%
33 49872
 
8.3%
22 31856
 
5.3%
41 31496
 
5.3%
34 31324
 
5.2%
82 27834
 
4.6%
36 26058
 
4.4%
38 20737
 
3.5%
85 19254
 
3.2%
Other values (42) 231560
38.7%
ValueCountFrequency (%)
1 2683
 
0.4%
2 10881
1.8%
3 2588
 
0.4%
4 289
 
< 0.1%
5 102
 
< 0.1%
11 1823
 
0.3%
12 1530
 
0.3%
13 12610
2.1%
14 618
 
0.1%
15 1194
 
0.2%
ValueCountFrequency (%)
93 16662
 
2.8%
92 6792
 
1.1%
91 64099
10.7%
88 635
 
0.1%
87 10091
 
1.7%
86 2622
 
0.4%
85 19254
 
3.2%
84 1860
 
0.3%
83 2524
 
0.4%
82 27834
4.6%

CRS_DEP_TIME
Real number (ℝ)

Distinct1224
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1329.975
Minimum4
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:42.001924image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile600
Q1910
median1321
Q31740
95-th percentile2125
Maximum2359
Range2355
Interquartile range (IQR)830

Descriptive statistics

Standard deviation492.43077
Coefficient of variation (CV)0.37025567
Kurtosis-1.0819534
Mean1329.975
Median Absolute Deviation (MAD)416
Skewness0.086503044
Sum7.9661245 × 108
Variance242488.06
MonotonicityNot monotonic
2024-03-30T03:03:42.345574image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 12486
 
2.1%
700 10379
 
1.7%
800 5712
 
1.0%
630 3742
 
0.6%
730 3683
 
0.6%
900 3672
 
0.6%
830 3647
 
0.6%
1000 3519
 
0.6%
1100 3427
 
0.6%
615 3053
 
0.5%
Other values (1214) 545648
91.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 5
 
< 0.1%
6 5
 
< 0.1%
9 3
 
< 0.1%
10 3
 
< 0.1%
13 2
 
< 0.1%
14 1
 
< 0.1%
15 32
< 0.1%
17 2
 
< 0.1%
18 14
< 0.1%
ValueCountFrequency (%)
2359 893
0.1%
2358 113
 
< 0.1%
2357 46
 
< 0.1%
2356 30
 
< 0.1%
2355 236
 
< 0.1%
2354 79
 
< 0.1%
2353 23
 
< 0.1%
2352 14
 
< 0.1%
2351 35
 
< 0.1%
2350 126
 
< 0.1%

DEP_TIME
Real number (ℝ)

Distinct1404
Distinct (%)0.2%
Missing2006
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean1332.1681
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:42.767913image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile559
Q1910
median1325
Q31745
95-th percentile2134
Maximum2400
Range2399
Interquartile range (IQR)835

Descriptive statistics

Standard deviation502.76017
Coefficient of variation (CV)0.37739995
Kurtosis-1.0369989
Mean1332.1681
Median Absolute Deviation (MAD)417
Skewness0.048880391
Sum7.9525372 × 108
Variance252767.79
MonotonicityNot monotonic
2024-03-30T03:03:43.102643image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 1660
 
0.3%
556 1446
 
0.2%
558 1379
 
0.2%
554 1358
 
0.2%
655 1331
 
0.2%
557 1330
 
0.2%
656 1267
 
0.2%
657 1265
 
0.2%
553 1235
 
0.2%
559 1199
 
0.2%
Other values (1394) 583492
97.4%
(Missing) 2006
 
0.3%
ValueCountFrequency (%)
1 68
< 0.1%
2 36
< 0.1%
3 41
< 0.1%
4 40
< 0.1%
5 33
< 0.1%
6 26
 
< 0.1%
7 37
< 0.1%
8 50
< 0.1%
9 38
< 0.1%
10 37
< 0.1%
ValueCountFrequency (%)
2400 49
 
< 0.1%
2359 87
< 0.1%
2358 102
< 0.1%
2357 95
< 0.1%
2356 113
< 0.1%
2355 129
< 0.1%
2354 127
< 0.1%
2353 133
< 0.1%
2352 136
< 0.1%
2351 116
< 0.1%

DEP_DELAY
Real number (ℝ)

ZEROS 

Distinct1000
Distinct (%)0.2%
Missing2016
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean7.5832965
Minimum-51
Maximum2596
Zeros28192
Zeros (%)4.7%
Negative372429
Negative (%)62.2%
Memory size4.6 MiB
2024-03-30T03:03:43.420526image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-51
5-th percentile-10
Q1-6
median-3
Q35
95-th percentile55
Maximum2596
Range2647
Interquartile range (IQR)11

Descriptive statistics

Standard deviation45.03301
Coefficient of variation (CV)5.9384478
Kurtosis355.49532
Mean7.5832965
Median Absolute Deviation (MAD)4
Skewness14.051782
Sum4526864
Variance2027.972
MonotonicityNot monotonic
2024-03-30T03:03:43.722910image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5 48682
 
8.1%
-4 44979
 
7.5%
-3 42766
 
7.1%
-6 40039
 
6.7%
-2 37858
 
6.3%
-7 34074
 
5.7%
-1 33645
 
5.6%
0 28192
 
4.7%
-8 27624
 
4.6%
-9 20193
 
3.4%
Other values (990) 238900
39.9%
ValueCountFrequency (%)
-51 1
 
< 0.1%
-43 1
 
< 0.1%
-41 1
 
< 0.1%
-39 1
 
< 0.1%
-36 2
 
< 0.1%
-35 2
 
< 0.1%
-34 4
< 0.1%
-33 1
 
< 0.1%
-32 5
< 0.1%
-31 3
< 0.1%
ValueCountFrequency (%)
2596 1
< 0.1%
2525 1
< 0.1%
2508 1
< 0.1%
2347 1
< 0.1%
2127 1
< 0.1%
2079 1
< 0.1%
2046 1
< 0.1%
2013 1
< 0.1%
1886 1
< 0.1%
1781 2
< 0.1%

TAXI_OUT
Real number (ℝ)

Distinct147
Distinct (%)< 0.1%
Missing2114
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean17.070518
Minimum1
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:44.068482image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q112
median15
Q320
95-th percentile32
Maximum163
Range162
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.2037868
Coefficient of variation (CV)0.48058218
Kurtosis16.581514
Mean17.070518
Median Absolute Deviation (MAD)4
Skewness2.8308935
Sum10188607
Variance67.302118
MonotonicityNot monotonic
2024-03-30T03:03:44.425533image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13 49856
 
8.3%
12 49737
 
8.3%
14 47079
 
7.9%
11 45133
 
7.5%
15 42817
 
7.1%
16 37647
 
6.3%
10 36580
 
6.1%
17 33077
 
5.5%
18 28476
 
4.8%
9 24465
 
4.1%
Other values (137) 201987
33.7%
ValueCountFrequency (%)
1 6
 
< 0.1%
2 12
 
< 0.1%
3 49
 
< 0.1%
4 225
 
< 0.1%
5 483
 
0.1%
6 2080
 
0.3%
7 6122
 
1.0%
8 13334
 
2.2%
9 24465
4.1%
10 36580
6.1%
ValueCountFrequency (%)
163 2
< 0.1%
162 1
< 0.1%
157 1
< 0.1%
153 1
< 0.1%
152 1
< 0.1%
151 1
< 0.1%
150 2
< 0.1%
147 2
< 0.1%
144 1
< 0.1%
142 2
< 0.1%

TAXI_IN
Real number (ℝ)

Distinct118
Distinct (%)< 0.1%
Missing2226
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean7.9846383
Minimum1
Maximum174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:44.745324image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median6
Q310
95-th percentile18
Maximum174
Range173
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.8169385
Coefficient of variation (CV)0.72851622
Kurtosis26.72157
Mean7.9846383
Median Absolute Deviation (MAD)2
Skewness3.6054879
Sum4764769
Variance33.836774
MonotonicityNot monotonic
2024-03-30T03:03:45.102613image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 87055
14.5%
5 85814
14.3%
6 72010
12.0%
7 58701
9.8%
3 49865
8.3%
8 45708
7.6%
9 35480
 
5.9%
10 28231
 
4.7%
11 21901
 
3.7%
12 17283
 
2.9%
Other values (108) 94694
15.8%
ValueCountFrequency (%)
1 785
 
0.1%
2 12119
 
2.0%
3 49865
8.3%
4 87055
14.5%
5 85814
14.3%
6 72010
12.0%
7 58701
9.8%
8 45708
7.6%
9 35480
5.9%
10 28231
 
4.7%
ValueCountFrequency (%)
174 1
< 0.1%
150 1
< 0.1%
149 1
< 0.1%
143 1
< 0.1%
134 1
< 0.1%
133 1
< 0.1%
130 1
< 0.1%
125 1
< 0.1%
124 1
< 0.1%
122 1
< 0.1%

CRS_ARR_TIME
Real number (ℝ)

Distinct1305
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1490.617
Minimum1
Maximum2359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:45.438673image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile720
Q11101
median1518
Q31925
95-th percentile2258
Maximum2359
Range2358
Interquartile range (IQR)824

Descriptive statistics

Standard deviation520.45962
Coefficient of variation (CV)0.34915718
Kurtosis-0.49222638
Mean1490.617
Median Absolute Deviation (MAD)412
Skewness-0.28510131
Sum8.9283187 × 108
Variance270878.22
MonotonicityNot monotonic
2024-03-30T03:03:45.743783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2359 3321
 
0.6%
2100 1993
 
0.3%
2000 1846
 
0.3%
2200 1835
 
0.3%
1915 1767
 
0.3%
1640 1726
 
0.3%
950 1699
 
0.3%
1620 1689
 
0.3%
1810 1688
 
0.3%
1700 1659
 
0.3%
Other values (1295) 579745
96.8%
ValueCountFrequency (%)
1 61
 
< 0.1%
2 199
 
< 0.1%
3 102
 
< 0.1%
4 122
 
< 0.1%
5 714
0.1%
6 163
 
< 0.1%
7 219
 
< 0.1%
8 99
 
< 0.1%
9 116
 
< 0.1%
10 417
0.1%
ValueCountFrequency (%)
2359 3321
0.6%
2358 850
 
0.1%
2357 754
 
0.1%
2356 487
 
0.1%
2355 1146
 
0.2%
2354 476
 
0.1%
2353 294
 
< 0.1%
2352 406
 
0.1%
2351 298
 
< 0.1%
2350 836
 
0.1%

ARR_TIME
Real number (ℝ)

Distinct1439
Distinct (%)0.2%
Missing2226
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1470.5372
Minimum1
Maximum2400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:46.095723image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile656
Q11050
median1507
Q31919
95-th percentile2252
Maximum2400
Range2399
Interquartile range (IQR)869

Descriptive statistics

Standard deviation535.02535
Coefficient of variation (CV)0.36382985
Kurtosis-0.41785493
Mean1470.5372
Median Absolute Deviation (MAD)421
Skewness-0.34077809
Sum8.7753133 × 108
Variance286252.12
MonotonicityNot monotonic
2024-03-30T03:03:46.441523image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1649 700
 
0.1%
1645 698
 
0.1%
1633 692
 
0.1%
1121 681
 
0.1%
1839 681
 
0.1%
1859 676
 
0.1%
1149 676
 
0.1%
1641 676
 
0.1%
2146 670
 
0.1%
1127 669
 
0.1%
Other values (1429) 589923
98.5%
(Missing) 2226
 
0.4%
ValueCountFrequency (%)
1 380
0.1%
2 361
0.1%
3 357
0.1%
4 342
0.1%
5 308
0.1%
6 300
0.1%
7 298
< 0.1%
8 295
< 0.1%
9 328
0.1%
10 276
< 0.1%
ValueCountFrequency (%)
2400 330
0.1%
2359 362
0.1%
2358 348
0.1%
2357 369
0.1%
2356 421
0.1%
2355 413
0.1%
2354 465
0.1%
2353 408
0.1%
2352 438
0.1%
2351 468
0.1%

ARR_DELAY
Real number (ℝ)

ZEROS 

Distinct1063
Distinct (%)0.2%
Missing2965
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.1862558
Minimum-77
Maximum2577
Zeros11398
Zeros (%)1.9%
Negative397756
Negative (%)66.4%
Memory size4.6 MiB
2024-03-30T03:03:46.757755image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-77
5-th percentile-28
Q1-16
median-7
Q35
95-th percentile52
Maximum2577
Range2654
Interquartile range (IQR)21

Descriptive statistics

Standard deviation46.4506
Coefficient of variation (CV)39.157323
Kurtosis311.76779
Mean1.1862558
Median Absolute Deviation (MAD)10
Skewness12.738059
Sum707012
Variance2157.6583
MonotonicityNot monotonic
2024-03-30T03:03:47.226720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12 18398
 
3.1%
-10 18261
 
3.0%
-11 18171
 
3.0%
-9 17786
 
3.0%
-13 17665
 
2.9%
-14 17328
 
2.9%
-8 17277
 
2.9%
-7 16896
 
2.8%
-15 16847
 
2.8%
-6 16218
 
2.7%
Other values (1053) 421156
70.3%
ValueCountFrequency (%)
-77 1
 
< 0.1%
-76 1
 
< 0.1%
-74 1
 
< 0.1%
-72 1
 
< 0.1%
-71 2
< 0.1%
-70 3
< 0.1%
-69 2
< 0.1%
-67 2
< 0.1%
-66 3
< 0.1%
-65 3
< 0.1%
ValueCountFrequency (%)
2577 1
< 0.1%
2513 1
< 0.1%
2511 1
< 0.1%
2338 1
< 0.1%
2120 1
< 0.1%
2075 1
< 0.1%
2053 1
< 0.1%
2023 1
< 0.1%
1877 1
< 0.1%
1772 1
< 0.1%

CANCELLED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0.0
596828 
1.0
 
2140

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1796904
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 596828
99.6%
1.0 2140
 
0.4%

Length

2024-03-30T03:03:47.509824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:03:47.721522image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 596828
99.6%
1.0 2140
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 1195796
66.5%
. 598968
33.3%
1 2140
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1195796
66.5%
. 598968
33.3%
1 2140
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1195796
66.5%
. 598968
33.3%
1 2140
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1195796
66.5%
. 598968
33.3%
1 2140
 
0.1%

CANCELLATION_CODE
Categorical

MISSING 

Distinct3
Distinct (%)0.1%
Missing596828
Missing (%)99.6%
Memory size4.6 MiB
A
1073 
B
944 
C
123 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2140
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA
2nd rowA
3rd rowA
4th rowA
5th rowA

Common Values

ValueCountFrequency (%)
A 1073
 
0.2%
B 944
 
0.2%
C 123
 
< 0.1%
(Missing) 596828
99.6%

Length

2024-03-30T03:03:47.944069image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:03:48.208958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
a 1073
50.1%
b 944
44.1%
c 123
 
5.7%

Most occurring characters

ValueCountFrequency (%)
A 1073
50.1%
B 944
44.1%
C 123
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2140
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 1073
50.1%
B 944
44.1%
C 123
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2140
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 1073
50.1%
B 944
44.1%
C 123
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2140
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 1073
50.1%
B 944
44.1%
C 123
 
5.7%

DIVERTED
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.6 MiB
0.0
598143 
1.0
 
825

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1796904
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 598143
99.9%
1.0 825
 
0.1%

Length

2024-03-30T03:03:48.422763image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T03:03:48.604542image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0.0 598143
99.9%
1.0 825
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1197111
66.6%
. 598968
33.3%
1 825
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1197111
66.6%
. 598968
33.3%
1 825
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1197111
66.6%
. 598968
33.3%
1 825
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1796904
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1197111
66.6%
. 598968
33.3%
1 825
 
< 0.1%

AIR_TIME
Real number (ℝ)

Distinct620
Distinct (%)0.1%
Missing2965
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean113.17189
Minimum8
Maximum691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:48.848958image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile36
Q163
median96
Q3142
95-th percentile266
Maximum691
Range683
Interquartile range (IQR)79

Descriptive statistics

Standard deviation69.481071
Coefficient of variation (CV)0.61394284
Kurtosis2.4408764
Mean113.17189
Median Absolute Deviation (MAD)38
Skewness1.4180581
Sum67450785
Variance4827.6192
MonotonicityNot monotonic
2024-03-30T03:03:49.179959image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65 5149
 
0.9%
63 5075
 
0.8%
57 5047
 
0.8%
55 5038
 
0.8%
60 5014
 
0.8%
66 5012
 
0.8%
58 5001
 
0.8%
59 4991
 
0.8%
56 4987
 
0.8%
64 4975
 
0.8%
Other values (610) 545714
91.1%
ValueCountFrequency (%)
8 3
 
< 0.1%
9 20
 
< 0.1%
10 15
 
< 0.1%
11 7
 
< 0.1%
12 2
 
< 0.1%
14 21
 
< 0.1%
15 30
 
< 0.1%
16 84
 
< 0.1%
17 173
< 0.1%
18 278
< 0.1%
ValueCountFrequency (%)
691 1
< 0.1%
672 1
< 0.1%
663 1
< 0.1%
661 1
< 0.1%
660 1
< 0.1%
657 1
< 0.1%
656 2
< 0.1%
654 1
< 0.1%
652 1
< 0.1%
650 1
< 0.1%

CARRIER_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct809
Distinct (%)0.9%
Missing505983
Missing (%)84.5%
Infinite0
Infinite (%)0.0%
Mean25.280637
Minimum0
Maximum2577
Zeros38137
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:49.542102image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q323
95-th percentile103
Maximum2577
Range2577
Interquartile range (IQR)23

Descriptive statistics

Standard deviation75.269564
Coefficient of variation (CV)2.9773603
Kurtosis167.81339
Mean25.280637
Median Absolute Deviation (MAD)5
Skewness10.368158
Sum2350720
Variance5665.5073
MonotonicityNot monotonic
2024-03-30T03:03:49.897761image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 38137
 
6.4%
1 1979
 
0.3%
2 1930
 
0.3%
3 1822
 
0.3%
15 1745
 
0.3%
6 1725
 
0.3%
4 1709
 
0.3%
5 1561
 
0.3%
7 1553
 
0.3%
16 1550
 
0.3%
Other values (799) 39274
 
6.6%
(Missing) 505983
84.5%
ValueCountFrequency (%)
0 38137
6.4%
1 1979
 
0.3%
2 1930
 
0.3%
3 1822
 
0.3%
4 1709
 
0.3%
5 1561
 
0.3%
6 1725
 
0.3%
7 1553
 
0.3%
8 1451
 
0.2%
9 1346
 
0.2%
ValueCountFrequency (%)
2577 1
< 0.1%
2508 1
< 0.1%
2338 1
< 0.1%
2120 1
< 0.1%
2046 1
< 0.1%
2013 1
< 0.1%
1877 1
< 0.1%
1772 1
< 0.1%
1747 1
< 0.1%
1688 1
< 0.1%

WEATHER_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct350
Distinct (%)0.4%
Missing505983
Missing (%)84.5%
Infinite0
Infinite (%)0.0%
Mean2.2483949
Minimum0
Maximum1175
Zeros90098
Zeros (%)15.0%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:50.309834image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum1175
Range1175
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.914801
Coefficient of variation (CV)11.08115
Kurtosis779.89296
Mean2.2483949
Median Absolute Deviation (MAD)0
Skewness24.199979
Sum209067
Variance620.74731
MonotonicityNot monotonic
2024-03-30T03:03:50.684196image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 90098
 
15.0%
17 60
 
< 0.1%
6 58
 
< 0.1%
16 57
 
< 0.1%
18 56
 
< 0.1%
1 55
 
< 0.1%
19 53
 
< 0.1%
2 49
 
< 0.1%
10 49
 
< 0.1%
5 49
 
< 0.1%
Other values (340) 2401
 
0.4%
(Missing) 505983
84.5%
ValueCountFrequency (%)
0 90098
15.0%
1 55
 
< 0.1%
2 49
 
< 0.1%
3 39
 
< 0.1%
4 45
 
< 0.1%
5 49
 
< 0.1%
6 58
 
< 0.1%
7 45
 
< 0.1%
8 47
 
< 0.1%
9 44
 
< 0.1%
ValueCountFrequency (%)
1175 1
< 0.1%
1094 1
< 0.1%
1074 1
< 0.1%
1061 1
< 0.1%
1042 1
< 0.1%
989 1
< 0.1%
986 1
< 0.1%
973 1
< 0.1%
972 1
< 0.1%
965 1
< 0.1%

NAS_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct291
Distinct (%)0.3%
Missing505983
Missing (%)84.5%
Infinite0
Infinite (%)0.0%
Mean8.8062806
Minimum0
Maximum1277
Zeros52619
Zeros (%)8.8%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:51.055694image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile37
Maximum1277
Range1277
Interquartile range (IQR)12

Descriptive statistics

Standard deviation21.389196
Coefficient of variation (CV)2.4288569
Kurtosis462.30809
Mean8.8062806
Median Absolute Deviation (MAD)0
Skewness13.300701
Sum818852
Variance457.49769
MonotonicityNot monotonic
2024-03-30T03:03:51.457554image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 52619
 
8.8%
1 2481
 
0.4%
2 1922
 
0.3%
15 1831
 
0.3%
3 1718
 
0.3%
16 1617
 
0.3%
4 1585
 
0.3%
17 1518
 
0.3%
5 1446
 
0.2%
6 1366
 
0.2%
Other values (281) 24882
 
4.2%
(Missing) 505983
84.5%
ValueCountFrequency (%)
0 52619
8.8%
1 2481
 
0.4%
2 1922
 
0.3%
3 1718
 
0.3%
4 1585
 
0.3%
5 1446
 
0.2%
6 1366
 
0.2%
7 1265
 
0.2%
8 1212
 
0.2%
9 1168
 
0.2%
ValueCountFrequency (%)
1277 1
< 0.1%
1168 1
< 0.1%
973 1
< 0.1%
922 1
< 0.1%
905 1
< 0.1%
891 1
< 0.1%
849 1
< 0.1%
657 1
< 0.1%
570 1
< 0.1%
553 1
< 0.1%

SECURITY_DELAY
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct93
Distinct (%)0.1%
Missing505983
Missing (%)84.5%
Infinite0
Infinite (%)0.0%
Mean0.14569017
Minimum0
Maximum281
Zeros92440
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:51.796536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum281
Range281
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7843792
Coefficient of variation (CV)19.111648
Kurtosis2148.8413
Mean0.14569017
Median Absolute Deviation (MAD)0
Skewness37.112563
Sum13547
Variance7.7527675
MonotonicityNot monotonic
2024-03-30T03:03:52.347712image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92440
 
15.4%
16 28
 
< 0.1%
17 25
 
< 0.1%
18 23
 
< 0.1%
10 21
 
< 0.1%
15 21
 
< 0.1%
8 20
 
< 0.1%
20 18
 
< 0.1%
1 17
 
< 0.1%
14 16
 
< 0.1%
Other values (83) 356
 
0.1%
(Missing) 505983
84.5%
ValueCountFrequency (%)
0 92440
15.4%
1 17
 
< 0.1%
2 9
 
< 0.1%
3 11
 
< 0.1%
4 16
 
< 0.1%
5 12
 
< 0.1%
6 11
 
< 0.1%
7 13
 
< 0.1%
8 20
 
< 0.1%
9 14
 
< 0.1%
ValueCountFrequency (%)
281 1
< 0.1%
173 1
< 0.1%
168 1
< 0.1%
160 1
< 0.1%
144 1
< 0.1%
135 1
< 0.1%
126 1
< 0.1%
119 1
< 0.1%
114 1
< 0.1%
113 1
< 0.1%

LATE_AIRCRAFT_DELAY
Real number (ℝ)

MISSING  ZEROS 

Distinct614
Distinct (%)0.7%
Missing505983
Missing (%)84.5%
Infinite0
Infinite (%)0.0%
Mean24.791837
Minimum0
Maximum2017
Zeros45634
Zeros (%)7.6%
Negative0
Negative (%)0.0%
Memory size4.6 MiB
2024-03-30T03:03:52.723008image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q328
95-th percentile107
Maximum2017
Range2017
Interquartile range (IQR)28

Descriptive statistics

Standard deviation58.179892
Coefficient of variation (CV)2.3467358
Kurtosis140.06562
Mean24.791837
Median Absolute Deviation (MAD)2
Skewness8.6746304
Sum2305269
Variance3384.8999
MonotonicityNot monotonic
2024-03-30T03:03:53.134650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45634
 
7.6%
15 1405
 
0.2%
16 1218
 
0.2%
17 1218
 
0.2%
18 1109
 
0.2%
19 1039
 
0.2%
20 1001
 
0.2%
21 1000
 
0.2%
14 928
 
0.2%
13 889
 
0.1%
Other values (604) 37544
 
6.3%
(Missing) 505983
84.5%
ValueCountFrequency (%)
0 45634
7.6%
1 647
 
0.1%
2 677
 
0.1%
3 653
 
0.1%
4 660
 
0.1%
5 716
 
0.1%
6 780
 
0.1%
7 746
 
0.1%
8 762
 
0.1%
9 792
 
0.1%
ValueCountFrequency (%)
2017 1
< 0.1%
1827 1
< 0.1%
1748 1
< 0.1%
1638 1
< 0.1%
1604 1
< 0.1%
1482 1
< 0.1%
1430 1
< 0.1%
1381 1
< 0.1%
1341 1
< 0.1%
1315 1
< 0.1%

Interactions

2024-03-30T03:03:10.729870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:08.479207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:14.628476image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:20.742079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:28.301733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:34.914840image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:42.422358image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:48.874588image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:55.236372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:01.967074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:08.930824image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:16.104203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:23.224798image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:29.941817image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:36.474631image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:42.889883image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:49.635133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:54.887181image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:00.193567image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:05.329207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:11.004498image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:08.966490image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:14.891917image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:21.075128image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:28.647811image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:35.294848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:42.772983image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:49.201519image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:55.594399image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:02.323676image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:09.341661image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:16.477289image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:23.559266image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:30.306807image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:36.788039image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:43.222985image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:49.885629image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:55.163371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:00.440235image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:05.595729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:11.403024image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:09.261378image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:15.168031image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:21.402406image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:28.949185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:35.673502image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:43.098528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:49.533971image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:55.923063image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:02.639264image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:09.728126image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:16.801376image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:23.886663image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:30.642285image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:37.098557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:43.559864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:50.171439image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:55.410578image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:00.670009image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:05.831254image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:11.685362image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:09.625572image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:15.502995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:21.820976image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:29.288649image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:36.133483image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:43.443247image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:49.838921image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:56.317706image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:02.988348image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:10.211187image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:17.176178image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:24.258666image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:30.992657image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:37.569427image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:43.913392image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:50.450794image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:55.807269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:00.949423image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:06.215668image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:11.926615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:09.929400image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:15.774462image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:22.194447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:29.604002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:36.539906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:43.758539image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:50.189787image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:56.656203image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:03.283596image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:10.588931image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:17.489899image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:24.569372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:31.339053image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:37.870317image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:44.293012image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:50.683293image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:56.072530image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:01.166119image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:06.495995image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:12.247878image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:10.451906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:16.108185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:22.536404image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:29.983020image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:36.946447image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:44.131543image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:50.572434image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:57.005918image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:03.642044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:10.993720image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:18.008067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:24.915364image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:31.689833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:38.242258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:44.656863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:50.933205image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:56.332088image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:01.412998image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:06.752245image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:12.494162image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:10.730372image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:16.386010image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:22.877973image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:30.352993image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:37.309800image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:44.462174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:50.861433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:57.326432image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:03.984212image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:11.324940image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:18.365906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:25.253536image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:32.027595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:38.546167image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:45.009820image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:02:56.584853image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:01.647650image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:03:12.728617image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:11.027638image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:16.649150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:23.203374image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:03:01.846632image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:01:23.551368image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:01:51.523132image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:02:04.643914image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:02:19.065448image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
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2024-03-30T03:02:35.811256image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:42.289735image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:49.102833image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:54.327000image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:59.652978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:04.768926image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:10.228414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:15.873814image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:14.307868image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:20.356770image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:27.725785image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:34.472057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:41.984848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:48.417615image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:01:54.824534image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:01.582111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:08.492159image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:15.667829image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:22.823456image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:29.547681image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:36.105782image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:42.513843image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:49.350345image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:54.597471image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:02:59.883044image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:05.026341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-03-30T03:03:10.469129image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-03-30T03:03:16.703057image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T03:03:19.991836image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
0110/2/2023 12:00:00 AM9E480011193CVGCincinnati, OHKentucky5212478JFKNew York, NYNew York2217551750.0-5.010.032.020102010.00.00.0NaN0.098.0NaNNaNNaNNaNNaN
1110/2/2023 12:00:00 AM9E480111193CVGCincinnati, OHKentucky5213487MSPMinneapolis, MNMinnesota631000951.0-9.012.08.011071042.0-25.00.0NaN0.091.0NaNNaNNaNNaNNaN
2110/2/2023 12:00:00 AM9E480211193CVGCincinnati, OHKentucky5211433DTWDetroit, MIMichigan4310241021.0-3.013.08.011391126.0-13.00.0NaN0.044.0NaNNaNNaNNaNNaN
3110/2/2023 12:00:00 AM9E480311193CVGCincinnati, OHKentucky5211433DTWDetroit, MIMichigan4313421338.0-4.011.014.015001447.0-13.00.0NaN0.044.0NaNNaNNaNNaNNaN
4110/2/2023 12:00:00 AM9E480414576ROCRochester, NYNew York2211433DTWDetroit, MIMichigan4312401247.07.013.013.014001358.0-2.00.0NaN0.045.0NaNNaNNaNNaNNaN
5110/2/2023 12:00:00 AM9E480515096SYRSyracuse, NYNew York2211433DTWDetroit, MIMichigan4313011259.0-2.014.09.014351418.0-17.00.0NaN0.056.0NaNNaNNaNNaNNaN
6110/2/2023 12:00:00 AM9E480611423DSMDes Moines, IAIowa6111433DTWDetroit, MIMichigan4317201713.0-7.015.09.020071954.0-13.00.0NaN0.077.0NaNNaNNaNNaNNaN
7110/2/2023 12:00:00 AM9E480715096SYRSyracuse, NYNew York2211433DTWDetroit, MIMichigan4317361728.0-8.012.012.019121849.0-23.00.0NaN0.057.0NaNNaNNaNNaNNaN
8110/2/2023 12:00:00 AM9E485112478JFKNew York, NYNew York2211433DTWDetroit, MIMichigan43700654.0-6.027.08.0901845.0-16.00.0NaN0.076.0NaNNaNNaNNaNNaN
9110/2/2023 12:00:00 AM9E485311433DTWDetroit, MIMichigan4312478JFKNew York, NYNew York2215591554.0-5.023.013.017581754.0-4.00.0NaN0.084.0NaNNaNNaNNaNNaN
DAY_OF_WEEKFL_DATEOP_UNIQUE_CARRIEROP_CARRIER_FL_NUMORIGIN_AIRPORT_IDORIGINORIGIN_CITY_NAMEORIGIN_STATE_NMORIGIN_WACDEST_AIRPORT_IDDESTDEST_CITY_NAMEDEST_STATE_NMDEST_WACCRS_DEP_TIMEDEP_TIMEDEP_DELAYTAXI_OUTTAXI_INCRS_ARR_TIMEARR_TIMEARR_DELAYCANCELLEDCANCELLATION_CODEDIVERTEDAIR_TIMECARRIER_DELAYWEATHER_DELAYNAS_DELAYSECURITY_DELAYLATE_AIRCRAFT_DELAY
598958710/29/2023 12:00:00 AMYX580410721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia38820816.0-4.015.012.010121002.0-10.00.0NaN0.079.0NaNNaNNaNNaNNaN
598959710/29/2023 12:00:00 AMYX580514492RDURaleigh/Durham, NCNorth Carolina3612953LGANew York, NYNew York22840836.0-4.017.017.010291012.0-17.00.0NaN0.062.0NaNNaNNaNNaNNaN
598960710/29/2023 12:00:00 AMYX580612953LGANew York, NYNew York2211278DCAWashington, DCVirginia3818301825.0-5.046.06.019592002.03.00.0NaN0.045.0NaNNaNNaNNaNNaN
598961710/29/2023 12:00:00 AMYX580810721BOSBoston, MAMassachusetts1311278DCAWashington, DCVirginia3815201518.0-2.020.018.017161716.00.00.0NaN0.080.0NaNNaNNaNNaNNaN
598962710/29/2023 12:00:00 AMYX580914122PITPittsburgh, PAPennsylvania2312953LGANew York, NYNew York22850843.0-7.015.08.01025958.0-27.00.0NaN0.052.0NaNNaNNaNNaNNaN
598963710/29/2023 12:00:00 AMYX581012478JFKNew York, NYNew York2211278DCAWashington, DCVirginia38955951.0-4.020.05.011231101.0-22.00.0NaN0.045.0NaNNaNNaNNaNNaN
598964710/29/2023 12:00:00 AMYX581110721BOSBoston, MAMassachusetts1314100PHLPhiladelphia, PAPennsylvania23930922.0-8.025.011.011141106.0-8.00.0NaN0.068.0NaNNaNNaNNaNNaN
598965710/29/2023 12:00:00 AMYX581312953LGANew York, NYNew York2211278DCAWashington, DCVirginia3814301427.0-3.056.04.016001618.018.00.0NaN0.051.00.00.018.00.00.0
598966710/29/2023 12:00:00 AMYX582414122PITPittsburgh, PAPennsylvania2311433DTWDetroit, MIMichigan4312001149.0-11.012.06.013151244.0-31.00.0NaN0.037.0NaNNaNNaNNaNNaN
598967710/29/2023 12:00:00 AMYX583811278DCAWashington, DCVirginia3812478JFKNew York, NYNew York2219452020.035.034.010.021192144.025.00.0NaN0.040.00.00.025.00.00.0